Search Results (28)

The building sector holds significant potential to mitigate climate change by adopting the circular economy. However, its transition is impeded by fragmented stakeholder engagement arising from complex socio-organisational dynamics. To address this, this article adopted the system dynamics (SD) modelling tool, which enables structured visualisation of the system while exploring and assessing stakeholder dynamics. The three-stage methodological approach includes the following: problem identification, building on the author’s prior publication, which identified the variables and their direct relationships; system conceptualisation, where the causal loop diagram was developed, followed by the identification of feedback loops, construction of the stock-flow diagram, and ultimately the SD model to capture indirect relationships; and model optimisation, which calibrated the SD model based on real-world circular building scenarios. The results revealed the stakeholder dynamics through their associated concerns. The results indicated that leveraging stakeholder dynamics within the complex system could foster collective action from fragmentation to enable the effective circular economy transition. This article proposes general and phase-specific actions tailored to each stakeholder, offering a structured framework for coordinated decision-making. These actions help bridge the gap between theory and practice, facilitating the sector’s transition from its current linear model toward a more collaborative and circular approach to climate change mitigation. Full article

This research explores the role of external feedback (provided by a foreign business community or FBC) in students’ learning. The aim is to determine whether indirect constructive feedback can sustain learning interest among disengaged students of upper secondary schools located in a large urban environment. The survey is constructed based on the Theory of Planned Behavior, while the feedback design stems from the Double-Loop Learning concept. Data were collected from 176 former and current students. The findings show that indirect constructive feedback has a significant influence on both attitude and paradigm (relating to learning). The findings show that indirect constructive feedback has a significant positive influence on both attitude and paradigm (relating to learning). Consistency, trust, teacher collaboration, and understanding students’ needs represent important factors when designing indirect constructive feedback from an external source. Full article

Sound wave fire suppression, an emerging firefighting technology, demonstrates unique potential by regulating the physicochemical processes of flames. This paper systematically reviews the research progress in acoustic fire extinguishing technology. Through a literature review and systematic comparison of existing methodologies, it reveals the core mechanisms of flame suppression: low-frequency sound waves (40–80 Hz) disrupt combustion stability via airflow disturbance, while high-frequency waves (>1 kHz) may rely on thermal effects or resonance mechanisms, with sound pressure and waveform significantly affecting extinguishing efficiency. Experimental results demonstrate that acoustic cavity focusing technology extends the effective fire suppression distance to 1.8 m while improving cooling efficiency by 10–20%. Integration with drone platforms and adaptive feedback systems enhances fire extinguishing energy efficiency by over 30%. When combined with water mist, this approach reduces suppression time to 30 s while mitigating sound pressure hazards. However, the critical parameters distinguishing sound-induced “flame enhancement” from “suppression” remain undefined, with insufficient research on adaptability to solid fuels and complex environments (microgravity, confined spaces), and a lack of high-temperature-resistant acoustic materials and multi-physics coupling models. Current fire suppression technologies predominantly rely on airflow disturbance-driven indirect mechanisms, whose stability remains questionable under extreme scenarios. Future advancements require breakthroughs in acoustic metamaterials, the integration of intelligent algorithms, and the collaborative optimization of multi-technology systems to facilitate the transition of acoustic wave-based fire suppression from laboratory settings to real-world industrial firefighting applications. Additionally, this study proposes an optimized solution that integrates acoustic waves with complementary fire suppression approaches, aiming to enhance overall firefighting effectiveness. Concurrently, an interdisciplinary research framework must be established to address the dual challenges of mechanistic elucidation and practical implementation. Full article

As the most widely distributed and largest terrestrial ecosystem in the world, grasslands play an important role in supporting global livestock production and maintaining ecosystem services. In light of the accelerated global socio-economic development and sustained population growth, grassland ecosystems are increasingly subjected to anthropogenic disturbances. However, there is a paucity of research examining the impact of such disturbances on plant–soil–microorganism interactions in grassland systems, particularly from the perspectives of grazing and tourism. Accordingly, this study presents a comprehensive analysis of the impacts of anthropogenic disturbance on grassland ecosystems over the past two decades, employing a dual perspective of grazing and tourism and utilizing econometric analysis of the existing literature through software such as CiteSpace. The results of this study demonstrate the following: (1) The current research focus is primarily concentrated in the fields of ecology and environmental sciences, particularly on the topics of plant diversity, abundance, and diversity, as well as the intensity of grazing. These areas may represent key development direction of future research. (2) The impact of anthropogenic disturbances on grassland ecosystems is primarily associated with grazing activities. Moderate grazing disturbances can facilitate the healthy development of grassland ecosystems. However, the intermediate disturbance hypothesis (IDH) may not fully account for the effects of grazing intensity on grassland ecology. At present, there is still a paucity of systematic research to determine the ecological indicators of grassland under a dual-disturbance scenario. It is recommended that future research be carried out to investigate the compound effects of trampling by tourism activities on plant–soil–microorganism interactions in grassland ecosystems. (3) The mutual feedback mechanism may represent a potential mechanism by which anthropogenic disturbances affect the coupled relationship between the plant, soil, and microbial systems in grassland ecosystems. Furthermore, the interaction among these three systems has the potential to exert direct or indirect impacts on the structure and function of grassland ecosystems in the context of disturbances. The present study aims to provide an overview of the structure and function of grassland ecosystems under anthropogenic disturbances. The objective is to identify a balance between the rational use of grassland and ecological protection under anthropogenic disturbance and to provide scientific reference for the sustainable use of grassland worldwide. Full article

We use high-resolution coupled atmosphere–ocean model simulations over the Gulf Stream extension region during a winter season to examine the upper ocean thermodynamic response to including current feedback to atmospheric wind stress. We demonstrate that a model that includes current feedback leads to significant changes in the structure and transport of heat throughout the upper ocean in comparison to the same model without current feedback. We find that including the current feedback leads to changes in the upper ocean temperature pattern that match the vorticity pattern. Areas with cyclonic ocean vorticity, typically north of the Gulf Stream extension, correspond to areas with warmer temperatures throughout the water column. Areas with anticyclonic ocean vorticity, typically south of the Gulf Stream extension, correspond to areas with cooler temperatures throughout the water column. We also find that including current feedback leads to an overall reduction in the submesoscale vertical heat flux spectra across all spatial scales, with differences in the submesoscale vertical heat flux corresponding to SST minus mixed layer temperature differences. The direct impact of current feedback on the thermodynamic structure within the upper ocean also has indirect impacts on other aspects of the ocean, such as the energy transfer between the ocean and the atmosphere, ocean stratification, and acoustic parameters. Full article

This paper addresses the problem of constructing a mathematical model of population density dynamics and the dynamics of forest areas damaged by spongy moth (Lymantria dispar L.) outbreaks in the United States, Europe, Russia, and Japan. The key variable of the model is either the pest population density or the area of forests damaged by spongy moths during a season. This variable can be considered proportional to the total current pest abundance in the study area. For the purposes of modeling, data from a number of different authors was used (see bibliography), as well as data from surveys conducted at the egg or caterpillar stage. The complexity of modeling the dynamics of L. dispar abundance is largely due to the fact that, when studying the dynamics of spongy moth population density, the values of external factors such as parasites, predators, and the amount of available food are often unknown. A simple model was proposed using only two types of data: population density and monthly weather characteristics. Our analysis demonstrated that, even in the absence of knowledge regarding the characteristics of ecosystem components interacting with the spongy moth population (parasites, predators, and the state of forage trees), it is possible to introduce models that characterize the regulatory processes in the population in terms of (i) the presence of negative and positive feedbacks in the system and (ii) the influence of external weather factors. The system under investigation was described as an autoregressive system, whereby the current state of the population is dependent on its state in previous years. The order of autoregression in the system was estimated using the order of the maximum significant partial autocorrelation function. It was found that the regulation of spongy moth population density was characterized by the presence of two feedback loops: positive feedback between the current population density and the population density in the previous season and negative feedback between the current population density and the population density two years ago. To evaluate the model, its stability margin was calculated and found to be directly proportional to the positive feedback coefficient and inversely proportional to the negative feedback coefficient. The model was demonstrated to explain up to 90% of the observed variance of real data. Although the model coefficients for different local populations (North America, Europe, and Asia) differ, the general form of the equation describing both direct data on population densities and indirect data on pest dynamics characterized by areas of stand damage is consistent. Consequently, the form of the ADL model is general, irrespective of the location of the local population. Full article

Using devices with microfluidic channels can allow for precise control over liquids flowing through them. Merging flows of immiscible liquids can create emulsions with highly monodispersed microdroplets within a carrier liquid, which are ideal for miniaturised reaction vessels which can be generated with a high throughput of tens of thousands of droplets per second. Control of the size and composition of these droplets is generally performed by controlling the pumping system pushing the liquids into the device; however, this is an indirect manipulation and inadequate if absolute precision is required in the size or composition of the droplets. In this work, we extend the previous development of image-based closed-loop feedback control over microdroplet generation to allow for the control of not only the size of droplets but also the composition by merging two aqueous flows. The feedback allows direct control over the desired parameters of volume and ratio of the two components over a wide range of ratios and outperforms current techniques in terms of monodispersity in volume and composition. This technique is ideal for situations where precise control over droplets is critical, or where a library of droplets of different concentrations but the same volume is required. Full article

In the turbulent construction market, the knowledge management process (KMP) is one of the most valuable tools of sustainability to help construction companies deal with dynamic changes and enhance their construction competitiveness (CC). To effectively utilize KMP in construction, this study aims to explore the interrelationships between key KMP factors and their influences on the CC, utilizing the structural equation modeling (SEM) approach. The objectives include extracting key KMP factors necessary for the CC enhancement, identifying direct and indirect relationships between the KMP and CC factors, and developing a self-assessment form to assist construction companies in evaluating their KMP performance and planning for long-term improvement. The results show that the five key KMP factors, namely knowledge utilization (KU), knowledge dissemination (KD), knowledge responsiveness (KR), knowledge storage (KS), and knowledge acquisition (KA), have direct and indirect effects on CC and that the feedback of CC is sent back to KMP factors for continuous improvement. The KU factor is crucial for short-term improvement. Construction companies should utilize stakeholders’ current practices and experiences to solve problems, conclude lessons learned, and pinpoint practices for future uses. The KS factor, on the other hand, should be emphasized for long-term plans to enhance KMP implementation and CC achievements. A long-term investment plan should be initiated in the database system to properly and effectively implement digital transformation in the 4.0 Era. The self-assessment form developed from the study results assists construction companies in assessing their KMP implementation and planning for sustainable development. Full article

This paper presents a practical implementation and measurement results of power-efficient chip performance optimization, utilizing low-cost indirect measurement methods to support self-X properties (self-calibration, self-healing, self-optimization, etc.) for in-field optimization of analog front-end sensory electronics with XFAB 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The reconfigurable, fully differential indirect current-feedback instrumentation amplifier (CFIA) performance is intrinsically optimized by employing a single test sinusoidal signal stimulus and measuring the total harmonic distortion (THD) at the output. To enhance the optimization process, the experience replay particle swarm optimization (ERPSO) algorithm is utilized as an artificial intelligence (AI) agent, implemented at the hardware level, to optimize the performance characteristics of the CFIA. The ERPSO algorithm extends the selection producer capabilities of the classical PSO methodology by incorporating an experience replay buffer to mitigate the likelihood of being trapped in local optima. Furthermore, the CFIA circuit has been integrated with a simple power-monitoring module to assess the power consumption of the optimization solution, to achieve a power-efficient and reliable configuration. The optimized chip performance showed an approximate 34% increase in power efficiency while achieving a targeted THD value of −72 dB, utilizing a 1 Vp-p differential input signal with a frequency of 1 MHz, and consuming approximately 53 mW of power. Preliminary tests conducted on the fabricated chip, using the default configuration pattern extrapolated from post-layout simulations, revealed an unacceptable performance behavior of the CFIA. Nevertheless, the proposed in-field optimization successfully restored the circuit’s performance, resulting in a robust design that meets the performance achieved in the design phase. Full article

Understanding greenhouse gas–climate processes and feedbacks is a fundamental step in understanding climate variability and its links to greenhouse gas fluxes. Chemical transport models are the primary tool for linking greenhouse gas fluxes to their atmospheric abundances. Hence, accurate simulations of greenhouse gases are essential. Here, we present a new simulation in the GEOS-Chem chemical transport model that couples the two main greenhouse gases—carbon dioxide (CO${}_2$) and methane (CH${}_4$)—along with the indirect greenhouse gas carbon monoxide (CO) based on their chemistry. Our updates include the online calculation of the chemical production of CO from CH${}_4$ and the online production of CO${}_2$ from CO, both of which were handled offline in the previous versions of these simulations. In the newly developed coupled (online) simulation, we used consistent hydroxyl radical (OH) fields for all aspects of the simulation, resolving biases introduced by inconsistent OH fields in the currently available uncoupled (offline) CH${}_4$, CO and CO${}_2$ simulations. We compare our coupled simulation with the existing v12.1.1 GEOS-Chem uncoupled simulations run the way they are currently being used by the community. We discuss differences between the uncoupled and coupled calculation of the chemical terms and compare our results with surface measurements from the NOAA Global Greenhouse Gas Reference Network (NOAA GGGRN), total column measurements from the Total Carbon Column Observing Network (TCCON) and aircraft measurements from the Atmospheric Tomography Mission (ATom). Relative to the standard uncoupled simulations, our coupled results suggest a stronger CO chemical production from CH${}_4$, weaker production of CO${}_2$ from CO and biases in the OH fields. However, we found a significantly stronger chemical production of CO${}_2$ in tropical land regions, especially in the Amazon. The model–measurement differences point to underestimated biomass burning emissions and secondary production for CO. The new self-consistent coupled simulation opens new possibilities when identifying biases in CH${}_4$, CO and CO${}_2$ source and sink fields, as well as a better understanding of their interannual variability and co-variation. Full article

Obesity is a pandemic caused by many factors, including a chronic excess in hypercaloric and high-palatable food intake. In addition, the global prevalence of obesity has increased in all age categories, such as children, adolescents, and adults. However, at the neurobiological level, how neural circuits regulate the hedonic consumption of food intake and how the reward circuit is modified under hypercaloric diet consumption are still being unraveled. We aimed to determine the molecular and functional changes of dopaminergic and glutamatergic modulation of nucleus accumbens (NAcc) in male rats exposed to chronic consumption of a high-fat diet (HFD). Male Sprague-Dawley rats were fed a chow diet or HFD from postnatal day (PND) 21 to 62, increasing obesity markers. In addition, in HFD rats, the frequency but not amplitude of the spontaneous excitatory postsynaptic current is increased in NAcc medium spiny neurons (MSNs). Moreover, only MSNs expressing dopamine (DA) receptor type 2 (D₂) increase the amplitude and glutamate release in response to amphetamine, downregulating the indirect pathway. Furthermore, NAcc gene expression of inflammasome components is increased by chronic exposure to HFD. At the neurochemical level, DOPAC content and tonic dopamine (DA) release are reduced in NAcc, while phasic DA release is increased in HFD-fed rats. In conclusion, our model of childhood and adolescent obesity functionally affects the NAcc, a brain nucleus involved in the hedonic control of feeding, which might trigger addictive-like behaviors for obesogenic foods and, through positive feedback, maintain the obese phenotype. Full article

Self-sensing actuation of shape memory alloy (SMA) means to sense both mechanical and thermal properties/variables through the measurement of any internally changing electrical property such as resistance/inductance/capacitance/phase/frequency of an actuating material under actuation. The main contribution of this paper is to obtain the stiffness from the measurement of electrical resistance of a shape memory coil during variable stiffness actuation thereby, simulating its self-sensing characteristics by developing a Support Vector Machine (SVM) regression and nonlinear regression model. Experimental evaluation of the stiffness of a passive biased shape memory coil (SMC) in antagonistic connection, for different electrical (like activation current, excitation frequency, and duty cycle) and mechanical input conditions (for example, the operating condition pre-stress) is done in terms of change in electrical resistance through the measurement of the instantaneous value. The stiffness is then calculated from force and displacement, while by this scheme it is sensed from the electrical resistance. To fulfill the deficiency of a dedicated physical stiffness sensor, self-sensing stiffness by a Soft Sensor (equivalently SVM) is a boon for variable stiffness actuation. A simple and well-proven voltage division method is used for indirect stiffness sensing; wherein, voltages across the shape memory coil and series resistance provide the electrical resistance. The predicted stiffness of SVM matches well with the experimental stiffness and this is validated by evaluating the performances such as root mean squared error (RMSE), the goodness of fit and correlation coefficient. This self-sensing variable stiffness actuation (SSVSA) provides several advantages in applications of SMA: sensor-less systems, miniaturized systems, simplified control systems and possible stiffness feedback control. Full article

The implementation of a fully-differential (FD) instrumentation amplifier (IA), based on indirect current feedback (ICF) and aimed to electrical impedance measurements in an Internet of Things (IoT) biomedical scenario, is presented. The IA consists of two FD transconductors, to process the input signal and feed back the output signal, a summing stage, used to add both contributions and generate the correcting current feedback signal, and a common-mode feedback network, which controls the DC level at the output nodes of the circuit. The transconductors are formed by a voltage-to-current conversion resistor and two voltage buffers, which are based on a super source follower cell in order to improve the overall response of the circuit. As a result, a compact single-stage structure, suitable for achieving a high bandwidth and a low power consumption, is obtained. The FD ICF IA has been designed and fabricated in 180 nm CMOS technology to operate with a 1.8-V supply and provide a nominal gain of 4 V/V. Experimental results show a voltage gain of 3.78 ± 0.06 V/V, a BW of 5.83 MHz, a CMRR at DC around 70 dB, a DC current consumption of 266.4 $\mathsf{\mu }$A and a silicon area occupation of 0.0304 mm${}^2$. Full article

With robotic-assisted minimally invasive surgery (RAMIS), patients and surgeons benefit from a reduced incision size and dexterous instruments. However, current robotic surgery platforms lack haptic feedback, which is an essential element of safe operation. Moreover, teleportation control challenges make complex surgical tasks like suturing more time-consuming than those that use manual tools. This paper presents a new force-sensing instrument that semi-automates the suturing task and facilitates teleoperated robotic manipulation. In order to generate the ideal needle insertion trajectory and pass the needle through its curvature, the end-effector mechanism has a rotating degree of freedom. Impedance control was used to provide sensory information about needle–tissue interaction forces to the operator using an indirect force estimation approach based on data-based models. The operator’s motion commands were then regulated using a hyperplanar virtual fixture (VF) designed to maintain the desired distance between the end-effector and tissue surface while avoiding unwanted contact. To construct the geometry of the VF, an optoelectronic sensor-based approach was developed. Based on the experimental investigation of the hyperplane VF methodology, improved needle–tissue interaction force, manipulation accuracy, and task completion times were demonstrated. Finally, experimental validation of the trained force estimation models and the perceived interaction forces by the user was conducted using online data, demonstrating the potential of the developed approach in improving task performance. Full article

A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems. Full article